Undergraduate Economic Review Volume 9 Issue 1 Article 2 2012 Does Player Performance Increase During the Postseason? A Look at Professional Basketball Jordan Weiss University of Southern California, [email protected] Follow this and additional works at: https://digitalcommons.iwu.edu/uer Recommended Citation Weiss, Jordan (2012) "Does Player Performance Increase During the Postseason? A Look at Professional Basketball," Undergraduate Economic Review: Vol. 9 : Iss. 1 , Article 2. Available at: https://digitalcommons.iwu.edu/uer/vol9/iss1/2 This Shorter Papers and Communications is protected by copyright and/or related rights. It has been brought to you by Digital Commons @ IWU with permission from the rights-holder(s). You are free to use this material in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This material has been accepted for inclusion by faculty at Illinois Wesleyan University. For more information, please contact [email protected]. ©Copyright is owned by the author of this document. Does Player Performance Increase During the Postseason? A Look at Professional Basketball Abstract This study examines the game logs of professional basketball players to determine whether they exhibit elevated performance during the postseason. In a survey of 10 players who were awarded the Most Valuable Player Award during the NBA Finals for the seasons 2001-02 thru 2010- 11, performance was found to be stable throughout the entire season. Implications for why player performance remains stable and why it believed that player performance increases during the postseason are discussed. This shorter papers and communications is available in Undergraduate Economic Review: https://digitalcommons.iwu.edu/uer/vol9/iss1/2 Weiss: Assessing Player Performance During the Postseason 1 Introduction Many sports journalists and commentators display excitement during the postsea- son, where it is believed that players elevate their game and showcase their very best talents. Some players, too, claim that their performance is better during the postsea- son. (New York Post, April 10, 2011). When elite teams experience a slump during the regular season, players and fans often create the impression that their skills will be much sharper come playoff time. Coaches, however, seem to disagree. Rather than play down a rough patch during the regular season, one coach had this to say: “You can’t turn the switch on and off like that in basketball without having to face some kind of price. Your game doesn’t just come back all of a sudden.” (LA Times, April 9, 2011). With players and fans united in the idea of elevated performance, and coaches feeling cause for concern during these slumps, who is most accurate at depicting the actual state of postseason performance? This study examines the game logs of 10 players who were awarded the Most Valuable Player Award during the NBA Finals for the seasons 2001-02 thru 2010- 11. Box score statistics for each game played are used, where data is available. The data are separated into five categories to determine whether an athletes’ perfor- mance increases during the postseason; specifically, Round 1 (First Round), Round 2 (Semi-Finals), Round 3 (Conference Finals), and Round 4 (NBA Finals). Perfor- mance during the regular season is used as a baseline as a means for comparison. By analyzing the relevant box score data, inference can be made as to whether player performance increases progressively throughout the playoffs. 2 Data All data in the sample come from Yahoo Sports, which publishes detailed player statistics and game logs. The ten players used in the sample are chosen based on having received the NBA Finals Most Valuable Player Award at least once during the period studied. If any player is capable of increased performance, we would expect it to be the leader of the team, who is generally the recipient of the award. The following individual data were collected for each player, with a description that follows: FG% (measures the number of shots made divided by the total shots attempted), 3PT% (measures the number of 3-Pointers made out of all attempted), FT% (measures the number of free throws made out of all attempted), Rbs (sum of offensive and defensive rebounds), Ast (when a player assists a teammate in the scoring of a point), TO (turn over), Stl (steal), Blk (blocked shot), PF (player foul), Pts (points). Published by Digital Commons @ IWU, 2012 1 Undergraduate Economic Review, Vol. 9 [2012], Iss. 1, Art. 2 3 Analysis I 3.1 Method Data for each player in the sample is separated into five categories (Regular Season, Round 1, Round 2, Round 3, Round 4), and the arithmetic average of box score data is computed for each category using all players in the sample. Because the number of minutes played per game varies by game, the per minute statistic (PM) is calculated by dividing the data for a given game by the number of minutes played in that game, with the exception of data measured in the form of a percentage. These results are available in Table 1. The results are also shown graphically in Figure 1, Figure 2, and Figure 3. Figure 1 displays the data for field goal percentage, three point field goal percentage, and free throw percentage. Figure 2 shows rebounds, assists, turnovers, and points on a per minute basis. Figure 3 shows steals, blocks, and personal fouls on a per minute basis. 3.2 Results and Discussion The averaged data do not indicate any drastic changes in overall player perfor- mance. Field goal percentage decreases during Rounds 1, 2, and 4 when compared to regular season performance. Three point field goal percentages are shown as increasing for these rounds. Points scored per minute are lower in Round 1 when compared to regular season performance, but are increasing throughout the post- season, with maximum performance matching regular season performance during Rounds 3 and 4. Table 1: Arithmetic mean for player statistics in each category y denotes the use of Per Minute Statistic Ptsy FG% 3PT% FT% Rbsy Asty TOy Stly Blky PFy Regular Season 0.64 49.09 32.46 78 0.2 0.12 0.07 0.03 0.03 0.07 Round 1 0.58 46.59 38.75 78.88 0.18 0.14 0.08 0.03 0.03 0.08 Round 2 0.61 45.99 40.25 75.54 0.18 0.11 0.07 0.03 0.03 0.07 Round 3 0.64 49.27 31.92 75.53 0.2 0.12 0.08 0.02 0.03 0.07 Round 4 0.64 46.99 35.95 80.31 0.22 0.1 0.08 0.03 0.03 0.07 https://digitalcommons.iwu.edu/uer/vol9/iss1/2 2 Weiss: Assessing Player Performance During the Postseason Figure 1 Figure 2 Published by Digital Commons @ IWU, 2012 3 Undergraduate Economic Review, Vol. 9 [2012], Iss. 1, Art. 2 Figure 3 4 Analysis II 4.1 Method To determine whether the performance of any individual player increased signif- icantly during the postseason, a total of nine1 equations are constructed for each player, modeling the box score statistic as the dependent variable y, in the follow- ing form: y = β0+β1minutes+ρ1Round 1+ρ2Round 2+ρ3Round 3+ρ4Round 4+"; where β0 is the intercept, β1 is the effect of minutes played in a given game, ρi represents the change in performance for Round i when compared to the Regular Season, and " is the residual. Each regression equation consists of a box score statistic which is used as the dependent variable (y), and independent variables consisting of the minutes played per game and four dummy variables used to distinguish between games played dur- ing the Regular Season, Round 1, Round 2, Round 3, and Round 4. The regular 1Some players, such as Centers, rarely attempt 3-point shots. This data is unavailable thus re- gressions are not computed. https://digitalcommons.iwu.edu/uer/vol9/iss1/2 4 Weiss: Assessing Player Performance During the Postseason season is used as the base period so that each round can be compared against it to observe the changes in performance. That is, β0 is the intercept for the Regular Sea- son, and ρi is the difference between the Regular Season and Round i performance (See Woolridge, Introductory Econometrics: A Modern Approach, 4th Edition). The arithmetic mean for the data coefficients are computed for each round after obtaining the results from the equations for each player(a total of 86 regressions). The results are available in Table 2. Table 3 shows the number of players in each category where the coefficient was statistically significant. Appendix 1 lists the coefficients for each box score statistic, for each player. 4.2 Results and Discussion The average coefficient2 for points scored per game in Rounds 1-3 are negative, which indicates that players scored fewer points in the first 3 rounds of the playoffs when compared to their regular season performance. Their performance exceeds their regular season performance in Round 4. Interestingly, while the points scored during Rounds 1-3 are less than the points scored during the regular season, the data indicate a rising trend beginning with Round 1 thru Round 4. So, although players did not score more points, on average, from the regular season to the post- season, they scored more points in succeeding rounds as they advanced through the playoffs. A similar pattern is seen with field goal percentage.